How a Large LATAM Police Force Uses NEXYTE to Identify Fake Identities and Stolen ID Numbers

Executive Summary

Identity-based fraud and the exploitation of stolen or fabricated ID numbers have become a pervasive challenge for modern law enforcement. A large LATAM police department, responsible for criminal investigations, public safety and judicial compliance, experienced a surge in identity-based scams, fraudulent benefit claims and fabricated personal profiles.

Although the agency had access to population registries, social security and benefits data, vehicle registration databases, COVID-era health datasets and other personal records, this data existed in disconnected silos, with varying levels of consistency. Investigators lacked a unified, reliable way to verify identities or detect contradictions across systems.

To address these constraints, the department deployed Cognyte's NEXYTE as a centralized data fusion and AI analytics solution. By applying machine learning-assisted identity validation, cross-dataset correlation, contradiction detection and data trust scoring, NEXYTE enabled investigators to surface suspicious patterns and uncover fraudulent identities in record time.

Instead of manually reconciling datasets, investigators relied on NEXYTE's automated analytical models (aligned with data protection and fairness requirements), to increase visibility, strengthen forensic confidence in identity matches and identify suspects who had evaded detection for years. NEXYTE didn't just fuse fragmented data, it created a single, trusted identity record for each individual, giving more confidence to investigators.

Challenges

Siloed and incompatible data repositories

The police department had access to many government datasets, but each was siloed:

• COVID-related personal records
• Social security and benefits data
• Vehicle ownership records
• Core population registries
• Miscellaneous records containing personal details with no official identifiers

Data quality varied, formats differed and conflicting records went undetected.

This caused:

• An inability to confirm that individuals were who they claimed to be
• Duplicate or overlapping identities across systems
• Low confidence in the accuracy and reliability of records
• Continued reliance on manual checks instead of analytic validation

Low data confidence and inconsistent identifiers

Many records were missing national ID numbers or used inconsistent formatting. Others contained:

• Misspelled names
• Outdated demographic information
• Mismatched details
• Conflicting addresses

Investigators had no systematic method to detect these inconsistencies or assess data reliability.

Operational blind spots, invisible criminals

Wrongdoers exploiting stolen IDs or fabricating synthetic ones:

• Received state benefits under fraudulent identities
• Avoided criminal warrants by using alternate profiles
• Registered vehicles using unassociated identities
• Created compartmentalized personal histories

The Operational Imperative

Leadership recognized a strategic threat: the inability to trust identity data meant they could not trust the investigations built on that data. They required a solution that could:

• Unify and fuse all identity-linked datasets
• Automate contradiction and anomaly detection
• Transform messy data into coherent identity graphs
• Establish a verified "one source of truth"
• Enable investigators to focus on investigation rather than data reconciliation

Solution

The agency deployed Cognyte's NEXYTE as a central source of trusted identity data above all existing data systems. NEXYTE ingested every available dataset and applied advanced ML-driven analytics, including:

• Correlating fragmented identity records across sources — Linking fragmented records for a single individual across sources
• Detecting conflicts and contradictions automatically — Identifying inconsistencies automatically in age, address, timestamps, document numbers, registrations, etc.
• Scoring data reliability across datasets — Assigning confidence scores to each dataset and record
• Cross-referencing data fields for consistency — Verifying consistency between COVID data, population registry, social security and vehicle data
• Detecting synthetic identities — Flagging records that statistically resemble artificially constructed personas
• Establishing "one source of truth" — Creating one verified, consolidated record per individual

The agency gained one unified reference point, as well as a visual investigative environment where investigators could see:

• Links between stolen ID holders
• Relationships between fraudulent registrants
• Connected vehicle ownership
• Suspicious behavior patterns
• Historical usage of fabricated identities

Outcome

The deployment of NEXYTE delivered clear operational results, including:

• Increased data clarity and identity certainty
• Identification of dozens of long-running fraud schemes
• Exposure of wrongdoers exploiting multiple identities and their locations
• Recovery of lost state funds
• Arrests of identity abusers who had evaded judicial detection for years
• A shift from manual record inspection to strategic intelligence work

Most importantly, the police force gained reliable identity intelligence — allowing them to act decisively rather than cautiously.

The Risk of Inaction - Avoided

Without modernization, the agency would have faced:

• Continued criminal exploitation of identity systems
• Systemic fraud undermining government benefits
• Inability to prosecute wrong-doers hiding behind false IDs
• Loss of institutional trust
• Erosion of investigative credibility

Instead, NEXYTE ensured that fragmented data was transformed into verified intelligence.